Sparse stochastic processes: A continuous-domain statistical framework for compressed sensing
Recorded 11 July 2013 in Lausanne, Vaud, Switzerland
Event: Spars 2013 - Signal Processing with Adaptive Sparse Structured Representations
We introduce an extended family of sparse processes that are speci?ed by a generic (non-Gaussian) innovation model or, equivalently, as solutions of linear stochastic differential equations driven by white Levy noise. We present the mathematical tools for their characterization. The two leading threads of the exposition are: the statistical property of in?nite divisibility, which induces two distinct types of behavior—Gaussian vs. sparse—at the exclusion of any other; the structural link between linear stochastic processes and splines. This allows us to prove that these processes admit a parsimonious representation in some matched wavelet-like basis. We show that these models have predictive power for image compression and that they are applicable to the derivation of statistical algorithms for solving ill-posed inverse problems, including compressed sensing.
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